The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.
The global variant datatable (right) can be filtered according to various criteria:
Variants in TIER 3 (right panel) can be interactively explored according to various criteria :
Variants in TIER 4 (right panel) can be interactively explored according to various criteria :
Noncoding variants (right panel) can be interactively explored according to various criteria :
NOTE: Reported biomarkers in CIViC/CGI are mapped at different resolutions (i.e. filter Biomarker mapping). The accuracy of a match between variants in the tumor sample and the reported biomarkers will vary accordingly (highlighted by gene symbols with different color backgrounds):
The plot below illustrates the mutational burden of indels in WES_PCB-2021-010-CTD-2021-D-00038 (black dashed line) along with the distribution in TCGA samples for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):
The plot below illustrates the fraction of indels among all calls in WES_PCB-2021-010-CTD-2021-D-00038(black dashed line) along with the distribution in TCGA for samples with known MSI status ( MSI.H = high microsatellite instability, MSS = microsatellite stable):
No variants found.
- NOT DETERMINED (ND): The number of SNV calls (n = 56) is too limited. A minimum of 200 SNVs is currently required for a mutational signature analysis to be performed.
- NOT DETERMINED (ND): The number of SNV calls (n = 56) is too limited. A minimum of 200 SNVs is currently required for a mutational signature analysis to be performed.
- NOT DETERMINED (ND): The number of SNV calls (n = 56) is too limited. A minimum of SNVs is currently required for a mutational signature analysis to be performed.
The report is generated with PCGR version 0.9.1.
Key report configuration settings:
Sample information:
Estimated properties on DNA cellularity and ploidy:
The report generated with PCGR is based on the following underlying tools and knowledge resources:
The prioritization of SNV and InDels found in the tumor sample is done according to a four-tiered structure, adopting the joint consensus recommendation by AMP/ACMG Li et al., 2017.
A complete list of reported biomarkers that associate with variants in the tumor sample (not necessarily qualifying for assignment to TIER 1/TIER 2) is also shown in a separate section.
Somatic copy number aberrations identified in the tumor sample are classified into two main tiers:
Included in the report is also a complete list of all oncogenes subject to amplifications, tumor suppressor genes subject to homozygous deletions, and other drug targets subject to amplifications
The set of somatic mutations observed in a tumor reflects the varied mutational processes that have been active during its life history, providing insights into the routes taken to carcinogenesis. Exogenous mutagens, such as tobacco smoke and ultraviolet light, and endogenous processes, such as APOBEC enzymatic family functional activity or DNA mismatch repair deficiency, result in characteristic patterns of mutation. Mutational signatures can have significant clinical impact in certain tumor types (Póti et al., 2019, Ma et al., 2018)
The MutationalPatterns package (Blokzijl et al., 2018) is used to estimate the relative contribution of known mutational signatures in a single tumor sample. MutationalPatterns makes an optimal reconstruction of the mutations observed in a given sample with a reference collection of n = 67 mutational signatures. By default, we restrict the signatures in the reference collection to those already observed in the tumor type in question (i.e. from large-scale de novo signature extraction on ICGC tumor samples).
NOTE: This sample contains too few variants for estimation of mutational signature contributions
Tumor mutational load or mutational burden is a measure of the number of mutations within a tumor genome, defined as the total number of mutations per coding area of a tumour genome. TMB may serve as a proxy for determining the number of neoantigens per tumor, which in turn may have implications for response to immunotherapy. For estimation of TMB, PCGR employs two different algorithms (one to be chosen by the user):
Numbers obtained with 1) or 2) is next divided by the coding target size of the sequencing assay.
Microsatellite instability (MSI) is the result of impaired DNA mismatch repair and constitutes a cellular phenotype of clinical significance in many cancer types, most prominently colorectal cancers, stomach cancers, endometrial cancers, and ovarian cancers (Cortes-Ciriano et al., 2017). We have built a statistical MSI classifier from somatic mutation profiles that separates MSI.H (MSI-high) from MSS (MS stable) tumors. The MSI classifier was trained using 999 exome-sequenced TCGA tumor samples with known MSI status (i.e. assayed from mononucleotide markers), and obtained a positive predictive value of 100% and a negative predictive value of 99.4% on an independent test set of 427 samples. Details of the MSI classification approach can be found here.
Note that the MSI classifier is applied only for WGS/WES tumor-control sequencing assays.
Kataegis describes a pattern of localized hypermutations identified in some cancer genomes, in which a large number of highly-patterned basepair mutations occur in a small region of DNA (ref Wikipedia). Kataegis is prevalently seen among breast cancer patients, and it also exists in lung cancers, cervical, head and neck, and bladder cancers, as shown in the results from tracing APOBEC mutation signatures (ref Wikipedia). PCGR implements the kataegis detection algorithm outlined in the KataegisPortal R package, applied in the study by Yin et al. (2020).
Explanation of key columns in the resulting table of potential kataegis events: